Created
September 4, 2019 11:36
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Reduce Pandas Memory Usage #python #pandas
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def reduce_mem_usage(df): | |
start_mem = df.memory_usage().sum() / 1024**2 | |
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) | |
for col in df.columns: | |
col_type = df[col].dtype | |
if col_type != object: | |
c_min = df[col].min() | |
c_max = df[col].max() | |
if str(col_type)[:3] == 'int': | |
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: | |
df[col] = df[col].astype(np.int8) | |
elif c_min > np.iinfo(np.uint8).min and c_max < np.iinfo(np.uint8).max: | |
df[col] = df[col].astype(np.uint8) | |
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: | |
df[col] = df[col].astype(np.int16) | |
elif c_min > np.iinfo(np.uint16).min and c_max < np.iinfo(np.uint16).max: | |
df[col] = df[col].astype(np.uint16) | |
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: | |
df[col] = df[col].astype(np.int32) | |
elif c_min > np.iinfo(np.uint32).min and c_max < np.iinfo(np.uint32).max: | |
df[col] = df[col].astype(np.uint32) | |
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: | |
df[col] = df[col].astype(np.int64) | |
elif c_min > np.iinfo(np.uint64).min and c_max < np.iinfo(np.uint64).max: | |
df[col] = df[col].astype(np.uint64) | |
else: | |
if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max: | |
df[col] = df[col].astype(np.float16) | |
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max: | |
df[col] = df[col].astype(np.float32) | |
else: | |
df[col] = df[col].astype(np.float64) | |
end_mem = df.memory_usage().sum() / 1024**2 | |
print('Memory usage after optimization is: {:.2f} MB'.format(end_mem)) | |
print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) | |
return df |
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